Sync from GitHub via hub-sync

LANGUAGES.md

@@ -26,7 +26,7 @@ _Sorted by strength of evidence, then coverage:_
 | [LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B) | 11 (9 European + zh/ja) | Declared, not benchmarked. [v1](https://huggingface.co/lightonai/LightOnOCR-1B-1025) is explicitly European/Latin-script only (9) |
 | [GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) | 8 (zh en fr es ru de ja ko) | Declared in card metadata only; no per-language evidence in the card body |
 | [HunyuanOCR-1.5](https://huggingface.co/tencent/HunyuanOCR) | "Multilingual" | Nothing enumerated, but explicitly targets **low-resource + ancient-script OCR** as a design goal (new in 1.5; the pinned 1.0 revision doesn't claim this) |
-| [dots.mocr](https://huggingface.co/rednote-hilab/dots.mocr) · [DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) / [-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2) · [Unlimited-OCR](https://huggingface.co/baidu/Unlimited-OCR) · [NuExtract3](https://huggingface.co/numind/NuExtract3) | "Multilingual", unspecified | A tag or one-liner only — no count, list, or benchmark |
+| [dots.mocr](https://huggingface.co/dots-studio/dots.mocr) · [DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) / [-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2) · [Unlimited-OCR](https://huggingface.co/baidu/Unlimited-OCR) · [NuExtract3](https://huggingface.co/numind/NuExtract3) | "Multilingual", unspecified | A tag or one-liner only — no count, list, or benchmark |
 | [olmOCR-2](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8) · [Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) · [SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview) · [LFM2.5-VL-Extract](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract) | English only | Stated on the card |
 | [Falcon-OCR](https://huggingface.co/tiiuae/Falcon-OCR) · [OvisOCR2](https://huggingface.co/ATH-MaaS/OvisOCR2) · [Xiaomi-OCR-0](https://huggingface.co/xiaomi-research/Xiaomi-OCR-0) · [LightOnOCR-3](https://huggingface.co/collections/lightonai/lightonocr-3) · [FireRed-OCR](https://huggingface.co/FireRedTeam/FireRed-OCR) · [ABot-OCR](https://huggingface.co/acvlab/ABot-OCR) · [RolmOCR](https://huggingface.co/reducto/RolmOCR) · [NuMarkdown-8B](https://huggingface.co/numind/NuMarkdown-8B-Thinking) · [lift](https://huggingface.co/datalab-to/lift) | Not stated | No language information on the card at all |
 

README.md

@@ -92,7 +92,7 @@ These are the maintained recipes. Each one has a tested `[tool.hf-jobs]` header,
 | [`hunyuan-ocr-1.5.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/hunyuan-ocr-1.5.py) | [HunyuanOCR-1.5](https://huggingface.co/tencent/HunyuanOCR) | 1B | 12 task types, including spotting, charts and translation | [Hunyuan Community](https://huggingface.co/tencent/HunyuanOCR/blob/main/LICENSE) (excludes EU, UK, South Korea) | `a10g-small` |
 | [`minicpm-ocr.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/minicpm-ocr.py) | [MiniCPM-OCR 1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0) | 1.3B | OpenBMB's full pipeline: layout regions, per-region prompts, Markdown with HTML tables; Hub dataset or Bucket in/out; runs on a public [`cr.hf.co`](https://huggingface.co/containers/davanstrien/minicpm-ocr) image (experimental) | Apache-2.0 | `a10g-large` |
 | [`dots-ocr.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/dots-ocr.py) | [dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) | 1.7B | 100+ languages; layout modes | MIT | `a10g-small` |
-| [`dots-mocr.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/dots-mocr.py) | [dots.mocr](https://huggingface.co/rednote-hilab/dots.mocr) | 3B | Eight prompt modes, including SVG from charts | MIT | `a10g-small` |
+| [`dots-mocr.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/dots-mocr.py) | [dots.mocr](https://huggingface.co/dots-studio/dots.mocr) | 3B | Eight prompt modes, including SVG from charts | MIT | `a10g-small` |
 | [`deepseek-ocr2-vllm.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/deepseek-ocr2-vllm.py) | [DeepSeek-OCR-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2) | 3B | Newer DeepSeek-OCR | Apache-2.0 | `a10g-small` |
 | [`unlimited-ocr2.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/unlimited-ocr2.py) | [Unlimited-OCR-2](https://huggingface.co/baidu/Unlimited-OCR-2) | 3.3B | Whole documents: 16 pages per request, paragraphs continue across page breaks; reads page folders or Buckets; resumable | MIT | `rtx-pro-6000` |
 | [`unlimited-ocr-vllm.py`](https://huggingface.co/datasets/uv-scripts/ocr/blob/main/unlimited-ocr-vllm.py) | [Unlimited-OCR](https://huggingface.co/baidu/Unlimited-OCR) | 3.3B | Markdown with layout boxes (`--strip-grounding` for clean text) | MIT | `a10g-small` |
@@ -176,7 +176,7 @@ Most dataset recipes also record the model and settings in an `inference_info` c
 | `lighton-ocr3.py` | `--model lightonai/LightOnOCR-3-4B\|lightonai/LightOnOCR-3-0.8B`, `--mode plain\|grounding`, `--blocks-column`, `--longest-edge` |
 | `hunyuan-ocr-1.5.py` | `--task-type` (12 types, default `doc_parse`), `--custom-prompt` |
 | `dots-ocr.py` | `--prompt-mode ocr\|layout-all\|layout-only` |
-| `dots-mocr.py` | `--prompt-mode` (8 modes). For SVG: `--model rednote-hilab/dots.mocr-svg --prompt-mode svg` |
+| `dots-mocr.py` | `--prompt-mode` (8 modes). For SVG: `--model dots-studio/dots.mocr-svg --prompt-mode svg` |
 | `deepseek-ocr-vllm.py` | `--prompt-mode document\|image\|free\|figure\|describe` |
 | `deepseek-ocr2-vllm.py` | `--prompt-mode document\|free` |
 | `unlimited-ocr-vllm.py` | `--strip-grounding`, `--grounding-column` |

SERVING.md

@@ -87,7 +87,7 @@ card/docs (verbatim commands live in the linked sources):
 |---|---|---|
 | lightonai/LightOnOCR-1B / 2-1B | ✅ vLLM | Card ships the serve command + client; ≥0.11.1 for v1; images longest-dim 1540px |
 | nanonets/OCR-s / OCR2-3B | ✅ vLLM | Bare `vllm serve` + client in card; **pin v0.10.2 for OCR2** (see above) |
-| rednote-hilab/dots.mocr | ✅ vLLM ≥0.11 | Direct from Hub id on `vllm/vllm-openai:v0.11.0`+ |
+| dots-studio/dots.mocr | ✅ vLLM ≥0.11 | Direct from Hub id on `vllm/vllm-openai:v0.11.0`+ |
 | rednote-hilab/dots.ocr | ✅ (GitHub) | HF card shows a legacy pre-0.11 hack; GitHub README: integrated upstream since 0.11 |
 | tencent/HunyuanOCR | ✅ vLLM 0.18.1 | serve.sh in repo; nightly adds DFlash speculative decoding |
 | zai-org/GLM-OCR | ✅ vLLM + SGLang + Ollama | Only card here with an SGLang serve example; needs vLLM nightly |

dots-mocr.py

@@ -33,10 +33,10 @@ Features:
 - Layout-aware text extraction
 - Web screen parsing
 - Scene text spotting
-- SVG code generation (use --prompt-mode svg, or --model rednote-hilab/dots.mocr-svg for best results)
+- SVG code generation (use --prompt-mode svg, or --model dots-studio/dots.mocr-svg for best results)
 
-Model: rednote-hilab/dots.mocr
-SVG variant: rednote-hilab/dots.mocr-svg
+Model: dots-studio/dots.mocr
+SVG variant: dots-studio/dots.mocr-svg
 vLLM: Officially integrated since v0.11.0
 GitHub: https://github.com/rednote-hilab/dots.mocr
 Paper: https://arxiv.org/abs/2603.13032
@@ -114,7 +114,7 @@ PROMPT_TEMPLATES = {
     "scene-spotting": """Detect and recognize the text in the image.""",
     "grounding-ocr": """Extract text from the given bounding box on the image (format: [x1, y1, x2, y2]).\nBounding Box:\n""",
     # SVG code generation — {width} and {height} are replaced with actual image dimensions.
-    # For best results, use --model rednote-hilab/dots.mocr-svg
+    # For best results, use --model dots-studio/dots.mocr-svg
     # Uses higher temperature (0.9) and top_p (1.0) per official recommendation.
     "svg": """Please generate the SVG code based on the image. viewBox="0 0 {width} {height}" """,
     "general": """ """,
@@ -306,7 +306,7 @@ def main(
     output_dataset: str,
     image_column: str = "image",
     batch_size: int = 16,
-    model: str = "rednote-hilab/dots.mocr",
+    model: str = "dots-studio/dots.mocr",
     max_model_len: int = 24000,
     max_tokens: int = 24000,
     gpu_memory_utilization: float = 0.9,
@@ -581,7 +581,7 @@ if __name__ == "__main__":
         print("   uv run --with vllm==0.30.0 dots-mocr.py input-dataset output-dataset")
         print("\n2. SVG generation:")
         print(
-            "   uv run --with vllm==0.30.0 dots-mocr.py charts svg-output --prompt-mode svg --model rednote-hilab/dots.mocr-svg"
+            "   uv run --with vllm==0.30.0 dots-mocr.py charts svg-output --prompt-mode svg --model dots-studio/dots.mocr-svg"
         )
         print("\n3. Web screen parsing:")
         print("   uv run --with vllm==0.30.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing")
@@ -614,7 +614,7 @@ Prompt Modes (official dots.mocr prompts):
 
 SVG Code Generation:
   Use --prompt-mode svg for SVG output. For best results, combine with
-  --model rednote-hilab/dots.mocr-svg (the SVG-optimized variant).
+  --model dots-studio/dots.mocr-svg (the SVG-optimized variant).
   SVG mode automatically uses temperature=0.9, top_p=1.0 unless overridden.
 
 Examples:
@@ -622,7 +622,7 @@ Examples:
   uv run --with vllm==0.30.0 dots-mocr.py my-docs analyzed-docs
 
   # SVG generation with optimized variant
-  uv run --with vllm==0.30.0 dots-mocr.py charts svg-out --prompt-mode svg --model rednote-hilab/dots.mocr-svg
+  uv run --with vllm==0.30.0 dots-mocr.py charts svg-out --prompt-mode svg --model dots-studio/dots.mocr-svg
 
   # Web screen parsing
   uv run --with vllm==0.30.0 dots-mocr.py screenshots parsed --prompt-mode web-parsing
@@ -650,8 +650,8 @@ Examples:
     )
     parser.add_argument(
         "--model",
-        default="rednote-hilab/dots.mocr",
-        help="Model to use (default: rednote-hilab/dots.mocr, or rednote-hilab/dots.mocr-svg for SVG)",
+        default="dots-studio/dots.mocr",
+        help="Model to use (default: dots-studio/dots.mocr, or dots-studio/dots.mocr-svg for SVG)",
     )
     parser.add_argument(
         "--max-model-len",

models.json

@@ -582,7 +582,7 @@
     "support_note": "Nanonets-OCR-s; use nanonets-ocr2.py."
   },
   "dots-mocr.py": {
-    "model_id": "rednote-hilab/dots.mocr",
+    "model_id": "dots-studio/dots.mocr",
     "params": "3B",
     "backend": "vllm",
     "task": "ocr | 8 prompt modes incl. SVG, layout + bbox",